Contagious defaults in a credit portfolio: a Bayesian network approach
| Autor: | Drona Kandhai, Ioannis Anagnostou, Sumit Sourabh, Javier Fermin Padilla Sanchez |
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| Přispěvatelé: | Computational Science Lab (IVI, FNWI) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Economics and Econometrics
Stylized fact Credit default swap Financial networks Computer science 05 social sciences Bayesian network 050109 social psychology 0502 economics and business Econometrics Portfolio 0501 psychology and cognitive sciences Default Graphical model Robustness (economics) 050203 business & management Finance Credit risk |
| Zdroj: | Journal of Credit Risk, 16(1), 1-26. Incisive Media Ltd. |
| ISSN: | 1755-9723 1744-6619 |
| Popis: | The robustness of credit portfolio models is of great interest for financial institutions and regulators, since misspecified models translate into insufficient capital buffers and a crisis-prone financial system. In this paper, the authors propose a method to enhance credit portfolio models based on the model of Merton by incorporating contagion effects. While, in most models, the risks related to financial interconnectedness are neglected, the authors use Bayesian network methods to uncover the direct and indirect relationships between credits while maintaining the convenient representation of factor models. A range of techniques to learn the structure and parameters of financial networks from real credit default swaps data are studied and evaluated. Their approach is demonstrated in detail in a stylized portfolio, and the impact on standard risk metrics is estimated. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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